This modules focuses on outcomes for kids living in poverty.
Use the Kids Count Data Book Online and examine how "Percent of children living in poverty" varies across the states.
Eyeballing the map, which region or regions of the country have a higher percentage of children living
in poverty?
[MAP:
poverty]
Use the ranking tool to find the variation in the percent of children living in poverty.
[RANKINGS: poverty]
Which state has the lowest(best) percent of children living in poverty, and what is the
percent in that state? Which state has the highest(worst) percent of children living in poverty, and
what is the percent in that state? Also, what is the range of variation between these percents?
| State Ranking |
Percent of children living in poverty |
Best
___________________ |
______% |
Worst
___________________ |
_____% |
| Variation (high-low percent) |
_____% |
Examine "percent of children living in poverty" as a trend over time. Choose the United States,
Minnesota, and one other state for your line graph. Consider choosing one of the worst of best-ranked states as a
comparison. Choose "percent children living in poverty" as your
indicator. Graph the data from 1990-1999.
[GRAPH]
Describe the overall national trend. Was there an increase, decrease, or did it stay the same. Put
another way, nationally are we experiencing more or less child poverty?
Describe the trend for Minnesota and the other state chosen. Do these state trends differ from the
national trend? Describe any similarities and differences.
Weber uses the term "life chances" to reveal how social class position influences the experiences that
people are more or less likely to have. People living in poverty may belong to the "working poor" or underclass, which some
sociologists use to refer to those on welfare. Poverty then means occupying a lower social class position. If we emphasize
the "life" of life chances, we would predict that living in poverty impacts death rates and other health indicators.
Write a hypothesis stating the relationship you predict between poverty and infant mortality.
To test the relationship between the percent of children living in poverty and
the infant mortality rate, open the excel file called "tool_us.xls".
Make a scatter plot by using the pull down menu. Let x be Poverty ("percent of children living in poverty 1999") and
y be "infant mortality 1999". Cut and paste the scatter plot into a Word file and record the
correlation coefficient.
Write an interpretation of the correlation coefficient summarizing the relationship between percent
children living in poverty and infant mortality. See below for an explanation of the correlation coefficient.
Are there any data points that seem to stand out --not part of the cluster of data points? These are
called outliers. Click on an outlier to see which state is represented.
Was the hypothesis confirmed? Explain your answer.
Repeat this analysis using "teen death rate 1999" as the new y variable.
Write a hypothesis stating the relationship you predict between poverty and the teen death rate.
To test the relationship between the percent of children living in poverty and
the teen death rate, open the excel file called "tool_us.xls".
Make a scatter plot by using the pull down menu. Let x be Poverty ("percent of children living in poverty 1999") and
y be Teendeaths ("teen death rate 1999"). Cut and paste the scatter plot into a Word file and record the
correlation coefficient. (An explanation of the correlation coefficient can be found below)
Write an interpretation of the correlation coefficient summarizing the relationship between percent
children living in poverty and teen death rate.
Are there any data points that seem to stand out --not part of the cluster of data points? These are
called outliers. Click on an outlier to see which state is represented.
Was the hypothesis confirmed? Explain your answer.
Explanation of Correlation Coefficient
The correlation coefficient or Pearson's r is a measure of the degree of linear association existing between two
variables. We want to pay close attention to both the direction and strength of the association. A positive correlation
is indicated by the absence of a negative sign and means that variables are changing in the same direction. An increase
or decrease in one variable corresponds to the same change in another variable. For example, we would expect that the
more time a students studies for an exam (x) the higher the exam score (y). A negative relationship is indicated by a
minus sign and means that as one variable increases there is a corresponding decrease in another variable. The strength
of a relationship is indicated by the numeric value of the coefficient. Coefficients range from 1.0 to -1.0. These values
are examples of perfect correlations. In reality most values are found in between 1.0 and -1.0. Correlations of .30 or
less (either + or -) are considered weak, .31 - .70 (either + or -) are deemed moderate and .71 and above (either + or -)
considered strong. These are not absolute rules but should be used as a guide in interpretation. Note that the higher the
correlation coefficient (either positive or negative), the more closely clustered the data points are in the shape of a
diagonal line.
Poverty also influences the health of children including both access to preventive care and future
health problems.
Develop a hypothesis accessing the relationship between "percent children living in poverty" and
"percent of two year olds immunized".
Test this relationship using the "tools_us.xls" file.
Make a scatter plot using the pull down menu. Let x be Poverty ("Percent of children living in poverty 1999") and y
be %2-Year-OldsImmunized ("Percent two year olds immunized 1999.") Cut and paste the scatter plot into a word file and
record the correlation coefficient.
Interpret your findings.
Choose your own variable that you think may be a health-related outcome of child poverty.
Develop a hypothesis accessing the relationship between child poverty (x) and
___________________________(y).
Test this relationship using the "tools_us.xls" file.
Make a scatter plot using the pull down menu. Let x be Poverty ("Percent of children living in poverty 1999") and y
be "___________________________ 1999." Cut and paste the scatter plot into a word file and
record the correlation coefficient.
Interpret your findings.
